Many researchers have proposed autonomous frameworks to screen pulmonary diseases using CXRs [1]}. The initial methods employed machine learning to recognize different chest abnormalities at the inference stage [2]}. However, these methods were confined to limited datasets and experimental settings due to the subjectiveness in their handcrafted features [3]}. With the advances in deep learning, the recent wave of diagnostic frameworks utilizes convolutional neural networks to screen and grade different chest abnormalities like pneumonia [4]}. Deep learning, although, increased the diagnostic performance of such frameworks by many folds. Still, they require exhaustive training efforts involving high computational power and large-scale (and well-annotated) data, and this limits their applicability towards screening new types of pathologies in a clinical setting. The incremental learning paradigm addresses this inherent limitation of deep neural networks. But incremental learning systems are vulnerable to catastrophic forgetting, which is defined as the inability of the classification model to forget its prior knowledge upon learning new tasks [5]}, [6]}. To overcome catastrophic forgetting within an incremental learning framework, many researchers have proposed knowledge distillation [7]} and contrastive learning [8]} based strategies. But these schemes ignore the structural similarities and interdependencies between different knowledge representations, which can significantly boost the classification performance of the incremental learning systems while showcasing high resistance to catastrophic forgetting.
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